Epidemiology
A machine-learning model adding Lp(a) predicts 1-year ASCVD risk better than smoking or diabetes status, validated in 53,930 patients (JACC Adv 2025)
Original title: Elevated Lipoprotein(a) Independently Increases Risk for Short-Term Atherosclerotic Cardiovascular Events in Machine Learning Predictive Models
This study used a derivation cohort of 731,983 individuals from a claims database to examine Lp(a)'s contribution to short-term atherosclerotic cardiovascular disease (ASCVD) risk prediction, an application the authors note is currently underutilised. Each 50 nmol/L rise in Lp(a) was independently associated with incident ASCVD (HR 1.072, 95% CI 1.059-1.084) and all-cause mortality (HR 1.041, 95% CI 1.015-1.068) after adjusting for age, sex and race/ethnicity. Novel machine-learning models incorporating Lp(a) predicted incident ASCVD events at 1, 2 and 3 years with strong discrimination (C-statistic 0.83-0.84) in both the derivation cohort and an independent validation cohort of 53,930 patients, though the 1-year model modestly underestimated risk in validation (calibration slope 1.25). Lp(a) contributed more to 1-year prediction than smoking, diabetes or other lipid parameters, and adding it improved the 1-year model's discrimination (IDI 0.03) and reclassification (NRI 10% at a 26% risk threshold), suggesting Lp(a) could help flag patients for near-term preventive escalation.
Original abstract
Background: Lipoprotein(a) [Lp(a)] is underutilized in short-term atherosclerotic cardiovascular disease (ASCVD) risk prediction.
Objectives: This study investigates Lp(a) contribution to short-term ASCVD event prediction using contemporary real-world data and machine learning (ML).
Methods: A cohort of 731,983 individuals from a claims database was used to investigate the association of Lp(a) with incident ASCVD and all-cause mortality using Cox proportional hazards models. Novel ML models were developed to predict incident ASCVD events at 1, 2, and 3 years after Lp(a) testing. The models were validated in an independent cohort of 53,930 patients.
Results: An increase of 50 nmol/L in Lp(a) was independently associated with incident ASCVD events (HR: 1.072; 95% CI: 1.059-1.084) and all-cause mortality (HR: 1.041; 95% CI: 1.015-1.068) after adjustment for age, sex, and race/ethnicity. Novel ML models featuring Lp(a) predicted incident ASCVD events at 1, 2, and 3 years with robust discrimination (C-statistic: 0.83-0.84) in both the derivation and validation cohorts. Modest underestimation of risk was observed in the validation cohort for the 1-year model (calibration slope 1.25). Lp(a) contributed more to 1-year ASCVD prediction than smoking, diabetes, and other lipid parameters. Inclusion of Lp(a) in the 1-year model led to an integrated discrimination improvement of 0.03 and an optimal net reclassification improvement of 10% at a risk threshold of 26%.
Conclusions: Lp(a) is a significant predictor of short-term ASCVD risk. Assessing Lp(a) and imminent ASCVD risk may assist in identifying patients who may benefit from escalation of preventative therapies.
Summary written by lp-a.org from the published abstract; figures as published. Page updated 17 August 2026. Methods.